tsne and bar plots
Fabio Sacher
28.02.2023
library(Seurat)
library(stringr)
library(tibble)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(patchwork)se_path <- c("Gg_D05_ctrl_seurat_070323",
"Gg_D07_ctrl_seurat_070323",
"Gg_ctrl_1_seurat_070323",
"Gg_ctrl_2_seurat_070323",
"Gg_ctrl_int_seurat_250723",
"Gg_lumb_1_seurat_070323",
"Gg_lumb_2_seurat_070323",
"Gg_lumb_int_seurat_250723",
"Gg_poly_1_seurat_070323",
"Gg_poly_2_seurat_070323",
"Gg_poly_int_seurat_250723",
"Gg_ctrl_lumb_int_seurat_250723",
"Gg_ctrl_poly_int_seurat_250723",
"Gg_all_int_seurat_270524")clust_col <- read.csv("~/spinal_cord_paper/annotations/broad_cluster_marker_colors.csv")tsne_plots <- list()
fine_tsne_plots <- list()
cell_numbers <- list()
for (i in seq(se_path)) {
# load the data sets
my.se <- readRDS(paste0("~/spinal_cord_paper/data/", se_path[i], ".rds"))
annot <- read.csv(list.files("~/spinal_cord_paper/annotations",
pattern = str_remove(se_path[i], "_seurat_\\d{6}"),
full.names = TRUE))
if(length(table(annot$number)) != length(table(my.se$seurat_clusters))) {
stop("Number of clusters must be identical!")
}
my.se
# rename for left join
annot <- annot %>%
mutate(fine = paste(fine, number, sep = "_")) %>%
mutate(number = factor(number, levels = 1:nrow(annot))) %>%
rename(seurat_clusters = number)
# add cluster annotation to meta data
my.se@meta.data <- my.se@meta.data %>%
rownames_to_column("rowname") %>%
left_join(annot, by = "seurat_clusters") %>%
column_to_rownames("rowname")
# colors to plot
broad_cols <- clust_col %>%
filter(broad_cluster %in% annot$broad) %>%
pull(color)
# plot
tsne_plots[[i]] <- DimPlot(
object = my.se,
reduction = "tsne",
group.by = "broad",
cols = broad_cols,
label = TRUE,
label.size = 5
) +
ggtitle(NULL)+
theme_void() +
NoLegend() +
annotate(
"text",
label = paste0("broad ", se_path[i]),
x = 0,
y = 0,
angle = 45
)
#fine clustering plots
fine_tsne_plots[[i]] <- DimPlot(
object = my.se,
reduction = "tsne",
group.by = "seurat_clusters",
cols = rainbow(length(table(my.se$seurat_clusters))),
label = TRUE,
label.size = 3
) +
ggtitle(NULL)+
theme_void() +
NoLegend() +
annotate(
"text",
label = paste0("fine ", se_path[i]),
x = 0,
y = 0,
angle = 45
)
# prepare bar plot
cell_numbers[[i]] <- data.frame(table(my.se$broad),
sample = factor(my.se@project.name)) %>%
dplyr::mutate(sample_size = sum(Freq))
}
rm(my.se, annot)
names(tsne_plots) <- str_remove_all(se_path, "_seurat_\\d{6}")
names(cell_numbers) <- str_remove_all(se_path, "_seurat_\\d{6}")
tmp <- do.call(rbind, cell_numbers) %>%
mutate(rel = Freq/sample_size) %>%
rev()
tmp$Var1 <- factor(
tmp$Var1,
levels =
c("progenitors",
"FP",
"RP",
"FP/RP",
"neurons",
"OPC",
"MFOL",
"pericytes",
"microglia",
"blood",
"vasculature"
)
)order_col <- match(levels(tmp$Var1), clust_col$broad_cluster)
bar <- ggplot(tmp, aes(
x = sample,
y = rel,
fill = factor(Var1, levels = rev(levels(Var1)))
)) +
geom_bar(stat = "identity",
position = "stack") +
scale_fill_manual(values = rev(clust_col$color[order_col])) +
labs(fill = "Cell types") +
ggtitle("relative cell numbers of broad clusters")
bar
ggsave(
filename = "~/spinal_cord_paper/figures/bar_plots.pdf",
width = 14,
plot = bar,
device = "pdf"
)Saving 14 x 7 in imagegrid.arrange(grobs = tsne_plots)
ggsave(
filename = "~/spinal_cord_paper/figures/tsne_plots.pdf",
width = 7, height = 7,
plot = marrangeGrob(tsne_plots, nrow=1, ncol=1, top = NULL)
)grid.arrange(grobs = fine_tsne_plots)
ggsave(
filename = "~/spinal_cord_paper/figures/fine_tsne_plots.pdf",
width = 7, height = 7,
plot = marrangeGrob(fine_tsne_plots, nrow=1, ncol=1, top = NULL)
)Supplementary figure 3
Code to plot broad and fine clusters of all individual non integrated data sets. Exports raster to save space.
for (i in seq(tsne_plots)) {
tsne_plots[[i]]$layers[[3]] <- NULL # remove annot text
tsne_plots[[i]]$layers[[2]] <- NULL # remove labels
fine_tsne_plots[[i]]$layers[[3]] <- NULL # remove annot text
}
pdf("~/spinal_cord_paper/figures/Supplementary_Figure_3.pdf", width = 10, height = 10)
tsne_plots[[1]] + tsne_plots[[2]] + tsne_plots[[3]] + tsne_plots[[4]] +
tsne_plots[[6]] + tsne_plots[[7]] + tsne_plots[[9]] + tsne_plots[[10]] +
fine_tsne_plots[[1]] + fine_tsne_plots[[2]] + fine_tsne_plots[[3]] + fine_tsne_plots[[4]] +
fine_tsne_plots[[6]] + fine_tsne_plots[[7]] + fine_tsne_plots[[9]] + fine_tsne_plots[[10]] +
plot_layout(ncol = 4, byrow = TRUE) +
plot_annotation(tag_levels = 'A')
dev.off()png
2 # Date and time of Rendering
Sys.time()[1] "2024-05-28 11:26:46 CEST"sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /scicore/soft/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblas_sandybridgep-r0.3.1.so
locale:
[1] en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.1.1 gridExtra_2.3 ggplot2_3.3.3 dplyr_1.0.10
[5] tibble_3.1.8 stringr_1.4.0 SeuratObject_4.0.2 Seurat_4.0.5
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-1 deldir_1.0-6
[4] ellipsis_0.3.2 ggridges_0.5.3 spatstat.data_3.0-0
[7] farver_2.1.0 leiden_0.3.9 listenv_0.8.0
[10] ggrepel_0.9.1 fansi_0.5.0 codetools_0.2-18
[13] splines_4.1.0 knitr_1.41 polyclip_1.10-0
[16] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.2
[19] png_0.1-7 uwot_0.1.10 shiny_1.6.0
[22] sctransform_0.3.3 spatstat.sparse_3.0-0 compiler_4.1.0
[25] httr_1.4.2 assertthat_0.2.1 Matrix_1.3-3
[28] fastmap_1.1.0 lazyeval_0.2.2 cli_3.4.1
[31] later_1.2.0 htmltools_0.5.1.1 tools_4.1.0
[34] igraph_1.2.6 gtable_0.3.0 glue_1.6.2
[37] RANN_2.6.1 reshape2_1.4.4 Rcpp_1.0.7
[40] scattermore_0.7 jquerylib_0.1.4 vctrs_0.5.1
[43] nlme_3.1-152 lmtest_0.9-38 xfun_0.34
[46] globals_0.16.2 mime_0.10 miniUI_0.1.1.1
[49] lifecycle_1.0.3 irlba_2.3.3 goftest_1.2-2
[52] future_1.30.0 MASS_7.3-54 zoo_1.8-9
[55] scales_1.1.1 spatstat.core_2.1-2 promises_1.2.0.1
[58] spatstat.utils_3.0-1 parallel_4.1.0 RColorBrewer_1.1-2
[61] yaml_2.2.1 reticulate_1.22 pbapply_1.4-3
[64] sass_0.4.0 rpart_4.1-15 stringi_1.6.2
[67] highr_0.9 rlang_1.0.6 pkgconfig_2.0.3
[70] matrixStats_0.58.0 evaluate_0.20 lattice_0.20-44
[73] ROCR_1.0-11 purrr_0.3.4 tensor_1.5
[76] labeling_0.4.2 htmlwidgets_1.5.3 cowplot_1.1.1
[79] tidyselect_1.2.0 parallelly_1.33.0 RcppAnnoy_0.0.19
[82] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
[85] generics_0.1.3 DBI_1.1.1 withr_2.4.2
[88] pillar_1.8.1 mgcv_1.8-35 fitdistrplus_1.1-6
[91] survival_3.2-11 abind_1.4-5 sp_1.4-5
[94] future.apply_1.7.0 KernSmooth_2.23-20 utf8_1.2.1
[97] spatstat.geom_3.0-3 plotly_4.10.0 rmarkdown_2.17
[100] grid_4.1.0 data.table_1.14.0 digest_0.6.27
[103] xtable_1.8-4 tidyr_1.1.3 httpuv_1.6.1
[106] munsell_0.5.0 viridisLite_0.4.0 bslib_0.2.5.1